For any social network, not just a federated one.

My thoughts: The way it works in big tech social networks is like this:

  1. **The organic methods: **
  • your followee shares something from a poster you don’t follow
  • someone you don’t follow comments on a post from someone you follow
  • you join a group or community and find others you currently don’t follow
  1. The recommendation engine methods: content you do not follow shows up, and you are likely to engage in it based on statistical models. Big tech is pushing this more and more.
  2. Search: you specifically attempt to find what you’re looking for through some search capability. Big tech is pushing against this more and more.

In my opinion, the fediverse covers #1 well already. But #1 has a bubble effect. Your followees are less likely to share something very drastically different from what you already have.

The fediverse is principally opposed to #2, at least the way it is done in big tech. But maybe some variation of it could be done well.

#3 is a big weakness for fediverse. But I am curious how it would ideally manifest. Would it be full text search? Semantic search? Or something with more machine learning?

  • jonne@infosec.pubEnglish
    8·
    11 hours ago

    I personally wouldn’t mind algorithmic recommendations if:

    • you can control or choose the algorithm
    • you can turn it off, or it turns off after you follow N amount of users

    Discovery is important when you’re initially signing up, but once you found the people you want to follow, you don’t really need it any more. It should just be there to help new users, essentially. As long as it’s open source and not run for profit, there’s not the traditional incentive to keep your eyeballs on the app like we see with the other networks.